In the financial industry, a variety of venues exist which allow securities such as stocks, bonds and derivatives to be traded. Example conventional trading venues include stock exchanges, which include physical stock exchanges, e.g., the New York Stock Exchange, as well as electronic stock exchanges, e.g., NASDAQ. In addition to stock exchanges, a variety of alternative trading systems (ATSs) have been approved by the Securities and Exchange Commission (SEC) for conducting trades outside of conventional stock exchanges. Included among the ATSs are electronic communication networks (ECNs), which match buy orders with sell orders.
In most of the venues mentioned above, orders are publicly disseminated upon arrival, e.g., as stock quotes. Consequently, these venues have not been attractive to traders who wish to prevent the leakage of info into the market which could adversely impact execution quality.
Instead, some traders have turned to crossing networks, another form of ATS. Crossing networks provide a form of trading known as “dark pools,” in which orders are not publicly disclosed. In a crossing network, complete anonymity may be provided since orders are never disclosed—even to other network subscribers. The only information disclosed about the orders is in the form of an execution message, e.g., a stock print, which occurs after the orders have been executed. Thus, no information is disclosed until after a trade has been completed. The stock print is made available to network subscribers, and also to the general public, e.g., via the Consolidated Tape System. Crossing networks may be analogized to a black box in which orders are placed; orders are revealed only after matching orders are successfully executed. In contrast, ECNs generally display unmatched orders externally, although the identity of the buyer/seller may remain hidden. Thus, crossing networks offer more anonymity than ECNs.
In an ideal crossing network, a high degree of liquidity exists because there is a readily available supply of buyers and sellers, and because the anonymity afforded results in minimal price fluctuation. In practice, crossing networks deviate from the ideal. One problem is that crossing networks are susceptible to manipulation, e.g., gaming. As an illustrative example, suppose a seller has just sold shares of a certain stock at a first venue. Based on this sale, the seller, suspecting that there may be a sudden demand for the stock, purchases additional shares from another venue and attempts to resell the additional shares for a profit at the first venue, thereby gaming the crossing network.
Another problem is the large number of crossing networks available. Because there are many crossing network venues, buyers and sellers are spread out and accomplishing trades of any appreciable size may involve trading on more than one crossing network. This reduces liquidity and lowers average trade size, which decreases while the average number of transactions required to trade large orders increases. Further, as the number of trades increases, so does the likelihood of becoming a victim of gaming, since trades become more visible. Further still, the dispersion of buyers and sellers may result in traders missing out on desirable trading opportunities, e.g., failure to meet at the right venue at the right time.
Accordingly, there exists a need for a more efficient method of conducting transactions.